CN110365059A - A kind of optical power prediction technique and device - Google Patents
A kind of optical power prediction technique and device Download PDFInfo
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- CN110365059A CN110365059A CN201910754268.7A CN201910754268A CN110365059A CN 110365059 A CN110365059 A CN 110365059A CN 201910754268 A CN201910754268 A CN 201910754268A CN 110365059 A CN110365059 A CN 110365059A
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- 238000010248 power generation Methods 0.000 claims abstract description 41
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- 230000001052 transient effect Effects 0.000 claims description 17
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- 238000004364 calculation method Methods 0.000 claims description 6
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- H02J3/385—
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
Abstract
The present invention provides a kind of optical power prediction technique and devices, obtain the weather forecast data in photovoltaic plant location in predetermined period；According to weather forecast data, the Meteorological Characteristics variable of each inverter is calculated；It respectively will be in the corresponding optimal optical power prediction model of Meteorological Characteristics variable input inverter place inverter group of each inverter, obtain optical power predicted value of each inverter in predetermined period, wherein, inverter group is that the power generation performance of foundation inverter is grouped, the corresponding optimal optical power prediction model of each inverter group；Optical power predicted value of each inverter in predetermined period is summarized, optical power predicted value of the photovoltaic plant in predetermined period is obtained.The present invention carries out inverter grouping from inverter level, constructs an optimal optical power prediction model for each inverter group, improves optical power predetermined speed while improving optical power predictablity rate.
Description
Technical field
The present invention relates to technical field of photovoltaic power generation, more particularly to a kind of optical power prediction technique and device.
Background technique
In recent years, as photovoltaic increases newly, installation amount is increasing, and the photovoltaic power generation quantity being connected to the grid is also increasing, but
It is that, since photovoltaic power generation quantity is uncertain, photovoltaic power generation is also increasing to the impact of the stability of power grid.In practical applications,
In order to reduce impact of the photovoltaic power generation to grid stability, needs to carry out Accurate Prediction to the generated output of photovoltaic plant, make electricity
Force system carries out power regulation according to the generated output that prediction obtains.
Optical power prediction modeling generally is carried out from entire photovoltaic plant level at present, defaults all photovoltaic panels in photovoltaic plant
Direction and angle are unified.But the direction and angle of the photovoltaic panel actually in photovoltaic plant are likely to different, such as mountainous region power station
Direction, the angles and positions of photovoltaic panel are possibly different from, and are caused even if the received effective irradiation of same model equipment not
Together, generated output is also different.Cause optical power prediction accurate as it can be seen that carrying out optical power prediction modeling from entire photovoltaic plant level
Rate is lower.
Summary of the invention
In view of this, improving the accurate of optical power prediction the present invention provides a kind of optical power prediction technique and device
Rate.
In order to achieve the above-mentioned object of the invention, specific technical solution provided by the invention is as follows:
A kind of optical power prediction technique, comprising:
Obtain the weather forecast data in photovoltaic plant location in predetermined period；
According to the weather forecast data, the Meteorological Characteristics variable of each inverter is calculated；
Respectively by the corresponding optimal optical power of inverter group where the Meteorological Characteristics variable input inverter of each inverter
In prediction model, optical power predicted value of each inverter in described predetermined period is obtained, wherein inverter group is according to inverse
What the power generation performance of change device was grouped, the corresponding optimal optical power prediction model of each inverter group；
Optical power predicted value of each inverter in described predetermined period is summarized, obtains photovoltaic plant described
Optical power predicted value in predetermined period.
Optionally, the method also includes:
According to the history data of each inverter, Historical Monitoring data, model in photovoltaic plant and put into operation the time, it is right
Inverter in photovoltaic plant is grouped.
Optionally, it is described according to the history data of each inverter in photovoltaic plant, Historical Monitoring data, model and
It puts into operation the time, the inverter in photovoltaic plant is grouped, comprising:
According to the history data and Historical Monitoring data of each inverter in photovoltaic plant, each inverter is calculated
Power generation performance characteristic variable；
The inverter in photovoltaic plant is divided at least one model group according to inverter model；
The inverter in each model group is divided at least one group that puts into operation according to the time of putting into operation of inverter；
The Euclidean distance between power generation performance characteristic variable in the group that puts into operation by calculating any two inverter, must take office
The similarity anticipated between two inverters；
Using default clustering algorithm, according to the similarity in each group that puts into operation between any two inverter, by each throwing
Fortune group is divided at least one inverter group.
Optionally, the history data and Historical Monitoring data according to each inverter in photovoltaic plant, calculates
The power generation performance characteristic variable of each inverter, comprising:
The derivative variable of each inverter is calculated according to the Historical Monitoring data of inverter each in photovoltaic plant, it is described to spread out
The amount of changing includes daily irradiation value；
By calculating the correlation between the AC power in daily irradiation value and history data, correlation is filtered out
Greater than the history data and Historical Monitoring data of the high correlation of threshold value；
It is rejected in the history data of high correlation and Historical Monitoring data and fault of converter or inverter announcement occurs
History data and Historical Monitoring data when alert, obtain effective history data and Historical Monitoring data；
According to the transient irradiation value and AC power in effective history data and Historical Monitoring data, draw each
The irradiation power curve of inverter；
The power generation performance characteristic variable of corresponding inverter is extracted from the irradiation power curve of each inverter.
Optionally, the method also includes:
A target inverter is selected from each inverter group respectively；
It is inverse to generate each target for Historical Monitoring data and the derivative variable according to each target inverter
Become the history Meteorological Characteristics variable of device；
The history Meteorological Characteristics variable according to each target inverter exchanges function with history data respectively
Mapping relations between rate construct optimal optical power prediction model for each inverter group.
Optionally, the history Meteorological Characteristics variable and history data according to each target inverter respectively
In AC power between mapping relations, construct optimal optical power prediction model for each inverter group, comprising:
The history Meteorological Characteristics variable according to each target inverter exchanges function in history data respectively
Rate constructs multiple optical power prediction models for each inverter group, the corresponding types of models of each optical power prediction model
It is different；
According to the error of fitting of the corresponding each optical power prediction model of each inverter group, model complexity and pre-
The time is surveyed, is each optimal optical power prediction model of inverter group selection.
Optionally, the method also includes:
In the case where monitoring that inverter breaks down or alerts, to light function of the photovoltaic plant in described predetermined period
Rate predicted value is modified.
Optionally, described in the case where monitoring that inverter breaks down or alerts, to photovoltaic plant in the prediction
Optical power predicted value in period is modified, comprising:
In the case where monitoring that inverter breaks down, determine the inverter to break down in described predetermined period
Optical power predicted value；
In the case where monitoring that inverter alerts, the generated output for the inverter for occurring alerting is calculated and in group
Do not occur the average value of the difference ratio between the generated output of the inverter alerted each, obtains the hair for the inverter for occurring alerting
Electric power loss ratio；
According to optical power predicted value of the inverter in described predetermined period to break down and there is the inverter alerted
Generated output lose ratio, optical power predicted value of the photovoltaic plant in described predetermined period is modified.
A kind of optical power prediction meanss, comprising:
Weather forecast data capture unit, for obtaining the weather forecast data in photovoltaic plant location in predetermined period；
Meteorological Characteristics variable calculation unit, for calculating the Meteorological Characteristics of each inverter according to the weather forecast data
Variable；
Optical power predicting unit, for respectively by inverter where the Meteorological Characteristics variable input inverter of each inverter
In the corresponding optimal optical power prediction model of group, optical power predicted value of each inverter in described predetermined period is obtained,
In, inverter group is that the power generation performance of foundation inverter is grouped, the corresponding optimal smooth function of each inverter group
Rate prediction model；
Optical power predicted value collection unit, for optical power predicted value of each inverter in described predetermined period into
Row summarizes, and obtains optical power predicted value of the photovoltaic plant in described predetermined period.
Optionally, described device further include:
Inverter grouped element, for history data, the Historical Monitoring number according to each inverter in photovoltaic plant
It according to, model and puts into operation the time, the inverter in photovoltaic plant is grouped.
Optionally, the inverter grouped element includes:
Power generation performance characteristic variable computation subunit, for the history data according to each inverter in photovoltaic plant
With Historical Monitoring data, the power generation performance characteristic variable of each inverter is calculated；
Model is grouped subelement, for the inverter in photovoltaic plant to be divided at least one type according to inverter model
Number group；
Put into operation time grouping subelement, for being divided the inverter in each model group according to the time of putting into operation of inverter
For at least one group that puts into operation；
Similarity calculation subelement, for being become in the group that puts into operation by calculating the power generation performance feature of any two inverter
Euclidean distance between amount obtains the similarity between any two inverter；
Inverter group is grouped subelement, for utilizing default clustering algorithm, according to any two inversion in each group that puts into operation
Each group that puts into operation is divided at least one inverter group by the similarity between device.
Optionally, the power generation performance characteristic variable computation subunit, is specifically used for:
The derivative variable of each inverter is calculated according to the Historical Monitoring data of inverter each in photovoltaic plant, it is described to spread out
The amount of changing includes daily irradiation value；
By calculating the correlation between the AC power in daily irradiation value and history data, correlation is filtered out
Greater than the history data and Historical Monitoring data of the high correlation of threshold value；
It is rejected in the history data of high correlation and Historical Monitoring data and fault of converter or inverter announcement occurs
History data and Historical Monitoring data when alert, obtain effective history data and Historical Monitoring data；
According to the transient irradiation value and AC power in effective history data and Historical Monitoring data, draw each
The irradiation power curve of inverter；
The power generation performance characteristic variable of corresponding inverter is extracted from the irradiation power curve of each inverter.
Optionally, described device further include:
Optical power prediction model construction unit, comprising:
Target inverter selects subelement, for selecting a target inverter from each inverter group respectively；
Meteorological Characteristics variable computation subunit, for Historical Monitoring data according to each target inverter and described
Derivative variable, generates the history Meteorological Characteristics variable of each target inverter；
Optical power prediction model constructs subelement, for the history Meteorological Characteristics respectively according to each target inverter
It is pre- to construct optimal optical power for each inverter group for the mapping relations between AC power in variable and history data
Survey model.
Optionally, the optical power prediction model constructs subelement, is specifically used for:
The history Meteorological Characteristics variable according to each target inverter exchanges function in history data respectively
Rate constructs multiple optical power prediction models for each inverter group, the corresponding types of models of each optical power prediction model
It is different；
According to the error of fitting of the corresponding each optical power prediction model of each inverter group, model complexity and pre-
The time is surveyed, is each optimal optical power prediction model of inverter group selection.
Optionally, described device further include:
Optical power predicted value amending unit, for monitor inverter break down or alarm in the case where, to photovoltaic
Optical power predicted value of the power station in described predetermined period is modified.
Optionally, the optical power predicted value amending unit, is specifically used for:
In the case where monitoring that inverter breaks down, determine the inverter to break down in described predetermined period
Optical power predicted value；
In the case where monitoring that inverter alerts, the generated output for the inverter for occurring alerting is calculated and in group
Do not occur the average value of the difference ratio between the generated output of the inverter alerted each, obtains the hair for the inverter for occurring alerting
Electric power loss ratio；
According to optical power predicted value of the inverter in described predetermined period to break down and there is the inverter alerted
Generated output lose ratio, optical power predicted value of the photovoltaic plant in described predetermined period is modified.
Compared with the existing technology, beneficial effects of the present invention are as follows:
Optical power prediction technique disclosed by the invention establishes optical power prediction model from inverter level, keeps optical power pre-
Survey is more fine and accurate, and is grouped by the power generation performance according to inverter, is that each inverter group constructs one most
Excellent optical power prediction model considerably reduces the quantity and operand of prediction model, is improving optical power predictablity rate
Optical power predetermined speed is improved simultaneously.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of invention for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is a kind of flow diagram of optical power prediction technique disclosed by the embodiments of the present invention；
Fig. 2 is a kind of flow diagram of inverter group technology disclosed by the embodiments of the present invention；
Fig. 3 is the irradiation power curve synoptic diagram of inverter disclosed by the embodiments of the present invention；
Fig. 4 is a kind of structural schematic diagram of optical power prediction meanss disclosed by the embodiments of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Inventors discovered through research that establishing optical power prediction model from inverter level, extracting influences inverter power generation
The various data of power are modeled, more fine and accurate to the prediction of optical power, but the inverter in large-sized photovoltaic power station
Quantity establishes optical power prediction model for every inverter, it is clear that increase the number of model up to hundreds even thousands of
Amount and operand, can take considerable time in model prediction, be difficult to meet reporting for the short-term and ultra-short term prediction of optical power
Time limit requires, and the present invention is grouped the inverter in photovoltaic plant by the power generation performance according to inverter, is each inverse
Become device group and construct an optimal optical power prediction model, optical power prediction is improved while improving optical power predictablity rate
Speed.
Referring to Fig. 1, the embodiment of the invention discloses a kind of optical power prediction techniques, specifically includes the following steps:
S101: the weather forecast data in photovoltaic plant location in predetermined period are obtained；
Wherein, weather forecast data may include environment temperature, ambient humidity, wind direction, wind speed, plane transient irradiation, tiltedly
Face transient irradiation, component backboard temperature etc..
Predetermined period is set according to forecast demand, as short-term forecast can be small for 24 hours futures, 48 hours or 72
When, ultra-short term prediction can be 4 hours following.
S102: according to the weather forecast data, the Meteorological Characteristics variable of each inverter is calculated；
According to above-mentioned weather forecast data, calculate the derivative variable of each inverter, as average daily temperature, external irradiation degree,
Average daily irradiation, day irradiation kurtosis, the day irradiation degree of bias, earth's surface irradiate variation lines with ground external irradiation maximum value ratio, clearness index, day
Number, day irradiation peak-to-average force ratio etc..
Weather forecast data and derivative variable form the Meteorological Characteristics variable of each inverter.
S103: respectively by the corresponding optimal light of inverter group where the Meteorological Characteristics variable input inverter of each inverter
In power prediction model, optical power predicted value of each inverter in described predetermined period is obtained；
Wherein, inverter group is that the power generation performance of foundation inverter is grouped, each inverter group corresponding one
The type of a optimal optical power prediction model, the optimal optical power prediction model of each inverter group can be identical, can also not
Together.
S104: optical power predicted value of each inverter in described predetermined period is summarized, photovoltaic plant is obtained
Optical power predicted value in described predetermined period.
Optical power predicted value of each inverter in predetermined period is identical in the same inverter group, therefore, by inversion
The optical power predicted value of single inverter is total light of the inverter group multiplied by the quantity of inverter in inverter group in device group
Power prediction value, the total optical power predicted value of each inverter group is light of the photovoltaic plant in described predetermined period with value
Power prediction value.
It should be noted that predicting it carrying out optical power using the corresponding optimal optical power prediction model of inverter group
Before, it is necessary first to the inverter in photovoltaic plant is grouped, then constructs an optimal optical power for each inverter group
Prediction model.
Specifically, according to the history data of inverter each in photovoltaic plant, Historical Monitoring data, model and putting into operation
Time is grouped the inverter in photovoltaic plant, referring to Fig. 2, inverter group technology is as follows:
S201: it according to the history data and Historical Monitoring data of each inverter in photovoltaic plant, calculates each inverse
Become the power generation performance characteristic variable of device；
The derivative variable of each inverter is calculated according to the Historical Monitoring data of inverter each in photovoltaic plant, it is described to spread out
The amount of changing includes daily irradiation value；
By calculating the correlation between the AC power in daily irradiation value and history data, correlation is filtered out
Greater than the history data and Historical Monitoring data of the high correlation of threshold value；
It is rejected in the history data of high correlation and Historical Monitoring data and fault of converter or inverter announcement occurs
History data and Historical Monitoring data when alert, obtain effective history data and Historical Monitoring data；
According to the transient irradiation value and AC power in effective history data and Historical Monitoring data, draw each
The irradiation power curve of inverter；
The power generation performance characteristic variable of corresponding inverter is extracted from the irradiation power curve of each inverter.
Wherein, plane transient irradiation either inclined-plane transient irradiation (when power station data includes inclined-plane transient irradiation, is adopted
With inclined-plane transient irradiation, plane transient irradiation is otherwise used) it is used as abscissa, the inverter ac performance number conduct at corresponding moment
Ordinate can obtain the irradiation power curve for being similar to straight line.
The each following index feature of inverter can be calculated according to each irradiation power curve numerical value: power is maximum
Value, power minimum, power mean value, power quartile value, power standard be poor, power variation coefficient, power irradiance ratio most
Big value, minimum value, mean value, standard deviation and the coefficient of variation, slope, intercept and the error sum of squares of least square method fitting etc..This
A little index features measure inverter power generation performance similarity jointly, remember that these index features are Ak, k=1,2,3 ....
S202: the inverter in photovoltaic plant is divided by least one model group according to inverter model；
S203: the inverter in each model group is divided by least one group that puts into operation according to the time of putting into operation of inverter；
Inverter of the time phase difference no more than one month (or other durations) that will such as put into operation is divided into the group that puts into operation.
S204: the Euclidean distance between power generation performance characteristic variable in the group that puts into operation by calculating any two inverter,
Obtain the similarity between any two inverter；
Wherein, the calculation method of the Euclidean distance between the power generation performance characteristic variable of two inverters is as follows:
Wherein, m is the number of power generation performance characteristic variable, Ak, k=1,2,3 ..., i and j are represented any two in the group that puts into operation
Platform inverter.
S205: will be every according to the similarity in each group that puts into operation between any two inverter using default clustering algorithm
A group that puts into operation is divided at least one inverter group.
Specifically, can determine grouping number using the evaluation index silhouette coefficient in clustering algorithm as evaluation index.
By taking certain large-scale mountainous region power station as an example, the realization process of above-mentioned inverter group technology is illustrated.
History data of all inverters in power station in the past period is obtained by power station operation platform, including is handed over
Flow power data and the Historical Monitoring data including plane transient irradiation of power station environment monitor, inclined-plane transient irradiation, ring
The categorical datas such as border temperature, component backboard temperature, ambient humidity, wind speed, wind direction.Various spread out is calculated further according to Historical Monitoring data
The amount of changing, such as inverter efficiency, average daily temperature, external irradiation degree, average daily irradiation, day irradiation kurtosis, day irradiate the degree of bias, earth's surface
The coefficient of variation is irradiated with ground external irradiation maximum value ratio, clearness index, day, day irradiates peak-to-average force ratio etc..The screening of base area external irradiation degree
The Historical Monitoring data on power station daytime out calculate daily irradiation and the correlation between inverter ac power, filter out correlation
High historical data, and reject inverter there are failure or alarm where the moment historical data.
After a series of screenings, we obtain inverter generated output of the inverter in the case where excluding itself disturbing factor.
Using plane transient irradiation either inclined-plane transient irradiation as abscissa, the inverter ac performance number at corresponding moment is sat as vertical
Mark obtains irradiation and power scatter plot.Fig. 3 be photovoltaic plant in 6 inverters by screening after inverter ac power with
The scatter plot of plane transient irradiation.
From the point of view of intuitive, inverter can be divided into four groups in Fig. 3, and one point of the first from left and the right side are one group in figure, and the second from left and the right side two are classified as
One group, a left side three and a left side four are respectively classified as one group.
Calculate following index feature: power maximum value, power minimum, power mean value, power quartile value, power standard
Difference, power variation coefficient, the maximum value of power irradiance ratio, minimum value, mean value, standard deviation and the coefficient of variation, least square method
Slope, intercept and the error sum of squares of fitting.The Euclidean distance for calculating these index features between inverter again, utilizes clustering algorithm
In silhouette coefficient as evaluation index, the result that obtains is consistent with the conclusion intuitively obtained, this also indicates that this inverter point
Group mode is feasible and effective.
After the completion of inverter grouping, a target inverter is selected from each inverter group respectively；According to each
The Historical Monitoring data of the target inverter and the derivative variable, the history for generating each target inverter are meteorological special
Levy variable；Respectively according to the AC power in the history Meteorological Characteristics variable of each target inverter and history data
Between mapping relations, construct optimal optical power prediction model for each inverter group.
Specifically, respectively in the history Meteorological Characteristics variable and history data according to each target inverter
AC power constructs multiple optical power prediction models for each inverter group, such as using more vertical first linear regressions, engineering
Practise algorithm, deep learning algorithm etc. establishes optical power prediction model, the corresponding types of models of each optical power prediction model is different.
According to error of fitting, model complexity and the predicted time of the corresponding each optical power prediction model of each inverter group,
For each optimal optical power prediction model of inverter group selection.The class of the optimal optical power prediction model of each inverter group
Type can be different.
Can error of fitting to each optical power prediction model, model complexity and predicted time quantization, and be respectively
It assigns weight, is weighted summation to error of fitting, model complexity and the predicted time of each optical power prediction model, will
The highest model of weighted sum value is determined as optimal optical power prediction model.
It should be noted that if needing to carry out the prediction of ultra-short term optical power to photovoltaic plant, real-time monitoring inverter is needed
Fault warning system, in the case where monitoring that inverter breaks down or alerts, to photovoltaic plant in described predetermined period
Optical power predicted value be modified.
In the case where monitoring that inverter breaks down, determine the inverter to break down in described predetermined period
Optical power predicted value, failure include the types such as disorderly closedown, PDP protection, the protection of main air blower failure, isolated island, usually can all be caused
Equipment downtime, optical power output are 0.
In the case where monitoring that inverter alerts, such as drop volume operation will lead to inverter generated output reduce but
Inverter shutdown is not will lead to.It calculates in the generated output and same group for the inverter for occurring alerting and does not occur the inversion alerted each
The average value of difference ratio between the generated output of device obtains the generated output loss ratio for the inverter for occurring alerting；
According to optical power predicted value of the inverter in described predetermined period to break down and there is the inverter alerted
Generated output lose ratio, optical power predicted value of the photovoltaic plant in described predetermined period is modified.
By being modified to optical power predicted value of the photovoltaic plant in predetermined period, optical power can be further improved
The accuracy rate of prediction.
Disclosed a kind of optical power prediction technique based on the above embodiment, it is pre- that the present embodiment correspondence discloses a kind of optical power
Device is surveyed, referring to Fig. 4, including:
Weather forecast data acquisition list 401, for obtaining the weather forecast data in photovoltaic plant location in predetermined period；
Meteorological Characteristics variable calculation unit 402, for calculating the meteorology of each inverter according to the weather forecast data
Characteristic variable；
Optical power predicting unit 403, for respectively will each inverter Meteorological Characteristics variable input inverter where it is inverse
Become in the corresponding optimal optical power prediction model of device group, obtains optical power prediction of each inverter in described predetermined period
Value, wherein inverter group is that the power generation performance of foundation inverter is grouped, and each inverter group corresponding one optimal
Optical power prediction model；
Optical power predicted value collection unit 404, for predicting optical power of each inverter in described predetermined period
Value is summarized, and optical power predicted value of the photovoltaic plant in described predetermined period is obtained.
Optionally, described device further include:
Inverter grouped element, for history data, the Historical Monitoring number according to each inverter in photovoltaic plant
It according to, model and puts into operation the time, the inverter in photovoltaic plant is grouped.
Optionally, the inverter grouped element includes:
Power generation performance characteristic variable computation subunit, for the history data according to each inverter in photovoltaic plant
With Historical Monitoring data, the power generation performance characteristic variable of each inverter is calculated；
Model is grouped subelement, for the inverter in photovoltaic plant to be divided at least one type according to inverter model
Number group；
Put into operation time grouping subelement, for being divided the inverter in each model group according to the time of putting into operation of inverter
For at least one group that puts into operation；
Similarity calculation subelement, for being become in the group that puts into operation by calculating the power generation performance feature of any two inverter
Euclidean distance between amount obtains the similarity between any two inverter；
Inverter group is grouped subelement, for utilizing default clustering algorithm, according to any two inversion in each group that puts into operation
Each group that puts into operation is divided at least one inverter group by the similarity between device.
Optionally, the power generation performance characteristic variable computation subunit, is specifically used for:
The derivative variable of each inverter is calculated according to the Historical Monitoring data of inverter each in photovoltaic plant, it is described to spread out
The amount of changing includes daily irradiation value；
By calculating the correlation between the AC power in daily irradiation value and history data, correlation is filtered out
Greater than the history data and Historical Monitoring data of the high correlation of threshold value；
It is rejected in the history data of high correlation and Historical Monitoring data and fault of converter or inverter announcement occurs
History data and Historical Monitoring data when alert, obtain effective history data and Historical Monitoring data；
According to the transient irradiation value and AC power in effective history data and Historical Monitoring data, draw each
The irradiation power curve of inverter；
The power generation performance characteristic variable of corresponding inverter is extracted from the irradiation power curve of each inverter.
Optionally, described device further include:
Optical power prediction model construction unit, comprising:
Target inverter selects subelement, for selecting a target inverter from each inverter group respectively；
Meteorological Characteristics variable computation subunit, for Historical Monitoring data according to each target inverter and described
Derivative variable, generates the history Meteorological Characteristics variable of each target inverter；
Optical power prediction model constructs subelement, for the history Meteorological Characteristics respectively according to each target inverter
It is pre- to construct optimal optical power for each inverter group for the mapping relations between AC power in variable and history data
Survey model.
Optionally, the optical power prediction model constructs subelement, is specifically used for:
The history Meteorological Characteristics variable according to each target inverter exchanges function in history data respectively
Rate constructs multiple optical power prediction models for each inverter group, the corresponding types of models of each optical power prediction model
It is different；
According to the error of fitting of the corresponding each optical power prediction model of each inverter group, model complexity and pre-
The time is surveyed, is each optimal optical power prediction model of inverter group selection.
Optionally, described device further include:
Optical power predicted value amending unit, for monitor inverter break down or alarm in the case where, to photovoltaic
Optical power predicted value of the power station in described predetermined period is modified.
Optionally, the optical power predicted value amending unit, is specifically used for:
In the case where monitoring that inverter breaks down, determine the inverter to break down in described predetermined period
Optical power predicted value；
In the case where monitoring that inverter alerts, the generated output for the inverter for occurring alerting is calculated and in group
Do not occur the average value of the difference ratio between the generated output of the inverter alerted each, obtains the hair for the inverter for occurring alerting
Electric power loss ratio；
According to optical power predicted value of the inverter in described predetermined period to break down and there is the inverter alerted
Generated output lose ratio, optical power predicted value of the photovoltaic plant in described predetermined period is modified.
Optical power prediction meanss disclosed in the present embodiment establish optical power prediction model from inverter level, make optical power
Prediction is more fine and accurate, and is grouped by the power generation performance according to inverter, constructs one for each inverter group
Optimal optical power prediction model considerably reduces the quantity and operand of prediction model, is improving optical power predictablity rate
While improve optical power predetermined speed.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.For device disclosed in embodiment
For, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is said referring to method part
It is bright.
It should also be noted that, herein, relational terms such as first and second and the like are used merely to one
Entity or operation are distinguished with another entity or operation, without necessarily requiring or implying between these entities or operation
There are any actual relationship or orders.Moreover, the terms "include", "comprise" or its any other variant are intended to contain
Lid non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that
There is also other identical elements in process, method, article or equipment including the element.
The step of method described in conjunction with the examples disclosed in this document or algorithm, can directly be held with hardware, processor
The combination of capable software module or the two is implemented.Software module can be placed in random access memory (RAM), memory, read-only deposit
Reservoir (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technology
In any other form of storage medium well known in field.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (10)
1. a kind of optical power prediction technique characterized by comprising
Obtain the weather forecast data in photovoltaic plant location in predetermined period；
According to the weather forecast data, the Meteorological Characteristics variable of each inverter is calculated；
The corresponding optimal optical power of inverter group where the Meteorological Characteristics variable input inverter of each inverter is predicted respectively
In model, optical power predicted value of each inverter in described predetermined period is obtained, wherein inverter group is according to inverter
Power generation performance be grouped, the corresponding optimal optical power prediction model of each inverter group；
Optical power predicted value of each inverter in described predetermined period is summarized, obtains photovoltaic plant in the prediction
Optical power predicted value in period.
2. the method according to claim 1, wherein the method also includes:
It according to the history data of each inverter, Historical Monitoring data, model in photovoltaic plant and puts into operation the time, to photovoltaic
Inverter in power station is grouped.
3. according to the method described in claim 2, it is characterized in that, the history fortune according to each inverter in photovoltaic plant
It row data, Historical Monitoring data, model and puts into operation the time, the inverter in photovoltaic plant is grouped, comprising:
According to the history data and Historical Monitoring data of each inverter in photovoltaic plant, the power generation of each inverter is calculated
Performance characteristic variable；
The inverter in photovoltaic plant is divided at least one model group according to inverter model；
The inverter in each model group is divided at least one group that puts into operation according to the time of putting into operation of inverter；
The Euclidean distance between power generation performance characteristic variable in the group that puts into operation by calculating any two inverter, obtains any two
Similarity between a inverter；
Using default clustering algorithm, according to the similarity in each group that puts into operation between any two inverter, by each group that puts into operation
It is divided at least one inverter group.
4. according to the method described in claim 3, it is characterized in that, the history fortune according to each inverter in photovoltaic plant
Row data and Historical Monitoring data calculate the power generation performance characteristic variable of each inverter, comprising:
The derivative variable of each inverter, the derivative change are calculated according to the Historical Monitoring data of inverter each in photovoltaic plant
Amount includes daily irradiation value；
By calculating the correlation between the AC power in daily irradiation value and history data, filters out correlation and be greater than
The history data and Historical Monitoring data of the high correlation of threshold value；
When there is fault of converter or inverter alarm in rejecting in the history data of high correlation and Historical Monitoring data
History data and Historical Monitoring data, obtain effective history data and Historical Monitoring data；
According to the transient irradiation value and AC power in effective history data and Historical Monitoring data, each inversion is drawn
The irradiation power curve of device；
The power generation performance characteristic variable of corresponding inverter is extracted from the irradiation power curve of each inverter.
5. according to the method described in claim 4, it is characterized in that, the method also includes:
A target inverter is selected from each inverter group respectively；
Historical Monitoring data and the derivative variable according to each target inverter generate each target inverter
History Meteorological Characteristics variable；
Respectively according between the AC power in the history Meteorological Characteristics variable and history data of each target inverter
Mapping relations, construct optimal optical power prediction model for each inverter group.
6. according to the method described in claim 5, it is characterized in that, the history according to each target inverter respectively
The mapping relations between AC power in Meteorological Characteristics variable and history data, it is optimal for each inverter group building
Optical power prediction model, comprising:
Respectively according to the AC power in the history Meteorological Characteristics variable and history data of each target inverter, it is
Each inverter group constructs multiple optical power prediction models, and the corresponding types of models of each optical power prediction model is different；
When according to the error of fitting of the corresponding each optical power prediction model of each inverter group, model complexity and prediction
Between, it is each optimal optical power prediction model of inverter group selection.
7. the method according to claim 1, wherein the method also includes:
It is pre- to optical power of the photovoltaic plant in described predetermined period in the case where monitoring that inverter breaks down or alerts
Measured value is modified.
8. the method according to the description of claim 7 is characterized in that described in the feelings for monitoring that inverter breaks down or alerts
Under condition, optical power predicted value of the photovoltaic plant in described predetermined period is modified, comprising:
In the case where monitoring that inverter breaks down, light function of the inverter to break down in described predetermined period is determined
Rate predicted value；
In the case where monitoring that inverter alerts, the generated output for the inverter for occurring alerting is calculated and with each in group
Do not occur the average value of the difference ratio between the generated output of the inverter alerted, obtains the power generation function for the inverter for occurring alerting
Rate loses ratio；
According to optical power predicted value of the inverter in described predetermined period to break down and there is the hair of the inverter alerted
Electric power loss ratio is modified optical power predicted value of the photovoltaic plant in described predetermined period.
9. a kind of optical power prediction meanss characterized by comprising
Weather forecast data capture unit, for obtaining the weather forecast data in photovoltaic plant location in predetermined period；
Meteorological Characteristics variable calculation unit, the Meteorological Characteristics for calculating each inverter according to the weather forecast data become
Amount；
Optical power predicting unit, for respectively by inverter group pair where the Meteorological Characteristics variable input inverter of each inverter
In the optimal optical power prediction model answered, optical power predicted value of each inverter in described predetermined period is obtained, wherein inverse
Become the power generation performance that device group is foundation inverter to be grouped, each inverter group corresponding one optimal optical power prediction
Model；
Optical power predicted value collection unit, for being converged to optical power predicted value of each inverter in described predetermined period
Always, optical power predicted value of the photovoltaic plant in described predetermined period is obtained.
10. device according to claim 9, which is characterized in that described device further include:
Inverter grouped element, for according to the history data of each inverter, Historical Monitoring data, type in photovoltaic plant
Number and put into operation the time, the inverter in photovoltaic plant is grouped.
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